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Abstract

Graphs are an important abstraction used in many scientific fields. With the magnitude of graph-structured data constantly increasing, effective data analytics requires efficient and scalable graph processing systems. Although HPC systems have long been used for scientific computing, people have only recently started to assess their potential for graph processing, a workload with inherent load imbalance, lack of locality, and access irregularity. We propose ShenTu, the first general-purpose graph processing framework that can efficiently utilize an entire petascale system to process multi-trillion edge graphs in seconds. ShenTu embodies four key innovations: hardware specializing, supernode routing, on-chip sorting, and degree-aware messaging, which together enable its unprecedented performance and scalability. It can traverse an unprecedented 70-trillion-edge graph in seconds. Furthermore, ShenTu enables the processing of a spam detection problem on a 12-trillion edge Internet graph, making it possible to identify trustworthy and spam web pages directly at the fine-grained page level.